Tonight, I did a very rapid exploration of the Reykjavik index, in the context of the #MakeupMonday challenge, which I discovered today.

This index was developped by Kantar to quantify how women are perceived as leaders compared to men.

The index varies between 0 and 100, the maximum representing a country in which people feel that women are as legitimate and competent as men over 20 professions. Needless to say that no country currently holds a 100 score. The index also takes into account the how men and women hold different perceptions of this legitimity.

The data are here provided for the G7 countries, and the index was estimated by surveying over a 10000 person.

The data

data <- read_xlsx("reykjavik_index.xlsx")

Some cleaning

G7_average <- data[data$Country == "G7 Average", 2]
G7_average2 <- data[data$Country == "G7 Average", 2][[1]]

data <- data %>% 
  rename(reykjavik_index = `Reykjavik Index `) %>% 
  mutate(Country = fct_reorder(Country, reykjavik_index)) %>% 
  filter(Country != "G7 Average") %>% 
  mutate(to_colour = ifelse(reykjavik_index < G7_average2,
                            "under", "over"))

The colourfull barplot

The instinctive first graph, a barplot. I colour the country, because I think it’s prettier.

data %>% 
  ggplot(aes(x = Country, y = reykjavik_index,
             fill = Country)) +
  
  geom_hline(yintercept = G7_average2,
             size = 1, linetype = "dashed", 
             colour = "grey50")  +
  geom_col() +
  coord_flip() +
  annotate(geom = "text",
           label = "G7 average",
           x = "Italy",
           y = 80,
           size = 5,
           colour = "grey50",
           #fontface = "bold",
           family = "Roboto Condensed") +
  
  ylim(0, 100) +
  
  labs(title = "Reykjavik index per country",
       subtitle = "Higher values represent less gendered views about leadership",
       fill = "",
       x = "",
       y = "Reykjavik index for leadership",
       caption = "\nSource: https://data.world/makeovermonday") +
  my_theme +
  theme(legend.position = "none") +
  scale_fill_manual(values = wes_palette("Zissou1", n = 8, type = "continuous"))

The less colourfull barplot

I don’t want to spend a lot of time on these plots, so I will stick with barplots tonight and not go into wild creations. Tonight I believe in simplicity (and lazyness)

data %>% 
  ggplot(aes(x = Country, y = reykjavik_index,
             fill = to_colour,
             #colour = to_colour,
             label = reykjavik_index)) +
  
  geom_hline(yintercept = G7_average2,
             size = 1, linetype = "dashed", 
             colour = "grey50")  +
    geom_col() +
  geom_text(aes(y = reykjavik_index - 3),
            colour = "white") +
  coord_flip() +
  annotate(geom = "text",
           label = "G7 average (66)",
           x = "Italy",
           y = 82,
           size = 5,
           colour = "grey50",
           #fontface = "bold",
           family = "Roboto Condensed") +
  
  annotate(geom = "text",
           label = "Countries with higher\nagreement that women\nare as suitable as men\nat leadership positions",
           x = "France",
           y = 87,
           size = 3,
           colour = "#3B9AB2",
           #fontface = "bold",
           family = "Roboto Condensed") +
  
  annotate(geom = "text",
           label = "Countries wich hold\nmore prejudices against\nwomen leaders",
           x = "Japan",
           y = 87,
           size = 3,
           colour = "#F21A00",
           #fontface = "bold",
           family = "Roboto Condensed") +
  
  geom_hline(yintercept = 100,
             colour = "black",
             size = 1) +
  
  ylim(0, 100) +
  
  labs(title = "Reykjavic Index - What people really think of women leaders",
       subtitle = "\nIn the best cases there is still more than 1/4 of the road to travel to reach\nequal confidence in men and women abilities to lead\n",
       fill = "",
       x = "",
       y = "Reykjavik index for leadership",
       caption = "\nSource: https://data.world/makeovermonday") +
  my_theme +
  theme(legend.position = "none",
        axis.ticks.y = element_blank()) +
  scale_fill_manual(values = wes_palette("Zissou1", n = 2, type = "continuous")) -> plot1

plot1

I want a slighlty interactive version of the plot, so I use the ggplotly() function from the plotly package.

ggplotly(plot1,
         tooltip = c("x", "y"))